Basic Data Analysis for Time Series with R

INDEX

 

Note: Locators followed by “f ” and “t” refer to figures and tables respectively.

• acf(),
  • autocovariance estimation coding
  • background
  • and spectrum
  • for white noise errors
• acos()
• AIC. See Akaike's information criteria
• Akaike's information criteria (AIC)
  • as cross-validation, NYC temperatures
  • model selection with
• anova()
• arima.sim()
• ARMA(2,2) model
• AR(m) filtering matrix
  • filtering information
  • linear algebra
  • and lm()
  • to model MA(3)
  • standard computations
• AR(1) model for irregular spacing
  • final analysis
  • method
  • motivation
  • results
  • sensitivity analysis
• AR(m) structure, residuals for
  • data display
  • filtering twice
• ar.yw()
• asin()
• Assumptions
  • equal variance
    • regression
    • two- sample t-test
  • independence
  • introduction
  • logarithmic transformations, illustration of
  • normality
    • heavy tails
    • left skew
    • right skewed
• atan()
• Autocorrelation
  • AR(1)
  • AR(2)
  • estimation
  • for MA(1) models
  • for MA(2) models
  • stationarity
• Autocovariance
  • AR(1)
  • AR(2)
  • ARMA(m,l) model
  • estimation, 37
  • properties
  • stationarity
  • white noise
• Autoregressive model of order 1, AR(1)
  • adjustments
    • implications
    • skip method
  • autocorrelation
  • autocovariance
  • definition
  • examples (stable and unstable models)
  • illustration
• Autoregressive model of order 2, AR(2)
  • autocorrelation
  • autocovariance
  • examples 46t
  • and power spectrum
  • preliminary facts
  • R code
  • simulating data

 

• Backshift operator
  • and ARMA(m,l) models
  • definition
  • examples
  • stationary condition for AR(1) model
• Bayesian information criteria (BIC)
• Best linear unbiased ...